Recipient Organization
NORTH CAROLINA STATE UNIV
(N/A)
RALEIGH,NC 27695
Performing Department
Population Health and Pathobiology
Non Technical Summary
Diseases in dairy cattle can affect reproductive outcomes in many ways, especially if the reproductive tract is involved (Galvão, 2012; Giuliodori et al., 2013; Lima et al. 2019). However, it seems that non-uterine diseases have the ability to prejudice reproductive outcomes in dairy cattle (Ribeiro et al., 2013). Diseases like mastitis and pneumonia may result in reduced embryo length (P = 0.02) at day 15 of gestation, as well as have shown a tendency for lower Interferon Tau (IFN - t) production by the embryo (Ribeiro et al., 2016). Thus, results have indicated that non-uterine diseases most likely alter somehow the endometrium or the uterine environment which would be necessary to support pregnancy (Ribeiro et al., 2013; Ribeiro et al., 2016).Reduced fertility has been described as a consequence of diseases, low body condition score (BCS), anoestrus, and their associations (Santos et al., 2009; Ribeiro et al. 2013). It is known that estradiol synthesis can be compromised during uterine infection by Escherichia coli as a consequence of the presence of lipopolysaccharide into the follicular fluid (Magata et al., 2014). Diseases seem to prejudice fertility independently whether it occurs near to the time of ovulation or not (Lussier et al., 1987; Ribeiro et al., 2016). When bacteria from an infection site reaches the bloodstream, causing what is called bacteremia, multiple organs can be affected (Deitch, 1992; Marques et al., 2013). If the injury upon the body elicits a severe or unbridle systemic inflammatory response, the functionality of multiple organs can be compromised (Deitch, 1992).Heifers diagnosed as having consolidated lungs, caused by respiratory diseases during the preweaning phase, have shown a tendency (P = 0.06) for lower pregnancy to the first service, as well as have shown lower hazard to pregnancy (HR = 0.7) (Teixeira et al., 2017). Cows experiencing multiple diseases during lactation have 65% less chance (P < 0.01; AOR = 0.35) to become pregnant at the first service than health cows (AOR = 1.00) (Ribeiro et al., 2013). Similarly, cows experiencing only one disease during lactation have been shown to have 37% less chance (P < 0.01; AOR = 0.63) to become pregnant at first service (Ribeiro et al., 2013).The orchestrate inflammatory process caused by diseases is mediated by cytokines, especially the Tumor Necrosis Factor (TNF), interleukin-1, and interleukin-6 (Deitch, 1992), and once the injury against the body evokes a systemic inflammatory process, many organs and tissues of the body can be affected (Nuytinck et al., 1988). The consequences of multiple diseases during the preweaning phase upon the productive life of dairy cattle, individually or combined, as it happens in a farm situation, have still to be elucidated. Also, to our knowledge, multiple diseases have not been used to build an economic model to predict profitability. Therefore, it is hypothesized that non-uterine diseases during the preweaning period can affect reproductive performance, increase culling rate, and decrease milk production. The objectives of the study are (1) determine the consequences of the most common diseases of the preweaning period of dairy heifers on reproductive performance, culling, and milk production during the first lactation; (2) determined the main variables to be taken into account in a culling strategy for dairy heifers; (3) build an economic model to enhance culling decision-making of dairy heifers at the weaning time.
Animal Health Component
(N/A)
Research Effort Categories
Basic
100%
Applied
(N/A)
Developmental
(N/A)
Goals / Objectives
The objectives of the study are (1) determine the consequences of the most common diseases of the preweaning period of dairy heifers on reproductive performance, culling, and milk production during the first lactation; (2) determine the main variables to be taken into account in a culling strategy for dairy heifers; (3) build an economic model to enhance culling decision-making of dairy heifers at the weaning time.
Project Methods
Data will be extracted from a unique dairy farm, located on the Northwest area of Florida (Bell, FL), which possesses over 6,500 lactating Holstein cows, averaging 90 pounds of milk per cow per day, and having a calving interval of 13 months.The data set will be analyzed by randomized complete block design. Pre-prepared randomization form will be designed for the data such that a total of 584 blocks containing two slots each (sick vs. health) will be created to represent both groups as preconized in Silva et al. (2015). Allocation of heifers to groups within the block will be performed randomly based on the day of disease and age of the heifer.The sample size was calculated using the Power procedure of SAS (version 9.4; SAS Institute Inc.; Cary, NC). Using α = 0.05 and β = 0.20, and 7 percentage units difference in fertility (e.g. 60 vs. 67%) at 30 days after first breeding, a total of 1168 animals in total is deemed necessary.The statistical models for analyses of data will include the fixed effects of group (sick vs. health), season, body weight at weaning (low vs. high), body weight at first AI (low vs. high), service number, and the respective interactions with the group. Blocks will be analyzed as a random effect. Additional covariates such as technician and breeding type (AI vs. ET) or other will be used when univariable analysis indicated statistical effect (P < 0.10).Binary responses will be analyzed by multivariable logistic regression using the GLIMMIX procedure of SAS. Intervals to first AI and to pregnancy will be analyzed by the Cox's proportional hazard model using the PHREG procedure of SAS. The adjusted hazard ratio (AHR) and the respective 95% CI will be calculated. The time variables used in the models will be the intervals in days between days 365 of age to pregnancy. The median and mean days to pregnancy will be obtained from the LIFETEST procedure of SAS. Continuous and discrete variables will be analyzed by ANOVA using the GLIMMIX procedure of SAS fitting a normal distribution. Tests for normality of residuals and homogeneity of variances were conducted for each dependent variable.Differences withP ≤ 0.05 will be considered significant and those with 0.05 < P ≤ 0.10 will be considered tendencies.